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PubMed2001

Linking genomics to immunotherapy by reverse immunology--'immunomics' in the new millennium.

The disclosure of the human genome sequence and rapid advances in genomic expression profiling have revolutionized our knowledge about molecular changes in malignant diseases. Rapidly growing gene expression databases and improvements in bioinformatics tools set the stage for new approaches using large-scale molecular information to develop specific therapeutics in cancer. On one hand, the ability to detect clusters of genes differentially expressed in normal and malignant tissue may lead to widely applicable targeting of defined molecular structures. On the other hand, analyzing the 'molecular fingerprint' of an individual tumor raises the possibility of developing customized therapeutics. One approach to use the emerging new datasets for the development of novel therapeutics is to identify genes that are specifically expressed in tumors as targets for immune intervention. This review will focus on the process from in silico analysis of expression databases and screening of potential candidate genes by bioinformatics to the in vitro and in vivo analysis to determine the immunogenicity of candidate tumor antigens. Basic biological principles of 'reverse immunology' as well as technical advantages and difficulties will be addressed.

Algorithms↗

PubMed2025

Misdetection of frameshifts in SARS-CoV-2 genomes: need for additional harmonisation and efficient monitoring of data workflows.

Five years after the outbreak of the SARS-CoV-2 pandemic in 2020, diagnostic laboratories have moved from massive sequencing of thousands of samples to routine surveillance of SARS-CoV-2 cases, as with all other respiratory viruses. Surveillance remains of paramount importance to prevent a further SARS-CoV-2 surge, as the virus has been shown to mutate rapidly and can render available drugs and vaccines ineffective. During the pandemic, several bioinformatics pipelines and workflows have been developed to streamline analysis, shorten turnaround time and ensure reproducibility. As the number of samples decreases, laboratories are moving towards more flexible sequencing strategies and optimizing the cost per sample. However, workflow redesigns, even if individual steps have proven successful time and time again, can lead to challenges when changes in a bioinformatics pipeline are introduced (e.g. version updates, implementation of new features, etc.), a new combination of viral mutations emerge or a change in wet-lab procedures leads to unpredictable results. Here, we present a report of misidentified frameshift mutations in the consensus sequence of SARS-CoV-2, which led to an incorrect assumption of mutations in the spike and nucleocapsid viral proteins with the potential to affect PCR detection or even antigen testing. This investigation exemplifies the need for better awareness of the challenges that can occur even when using routinely applied protocols and analytical workflows and highlights the need for cooperation between experts of NGS, bioinformaticians and decision-makers towards more harmonized data workflows.

SARS-CoV-2↗

PubMed2024

Diagnostic yield of exome sequencing-based copy number variation analysis in Mendelian disorders: a clinical application.

Next-generation sequencing (NGS) coupled with bioinformatic tools has revolutionized the detection of copy number variations (CNVs), which are implicated in the emergence of Mendelian disorders. In this study, we evaluated the diagnostic yield of exome sequencing-based CNV analysis in 449 patients with suspected Mendelian disorders. We aimed to assess the diagnostic yield of this recently utilized method and expand the clinical spectrum of intragenic CNVs. The cohort underwent whole exome sequencing (WES) and clinical exome sequencing (CES). Using GATK-gCNV, we identified 12 pathogenic CNVs that correlated with their clinical findings and resulting in a diagnostic yield of 2.67%. Importantly, the study emphasizes the role of CNVs in the etiology of Mendelian disorders and highlights the value of exome sequencing-based CNV analysis in routine diagnostic processes.

Humans↗

PubMed2004

Computational analysis of responsible dipeptides for optimum pH in G/11 xylanase.

A bioinformatics method was used to search the responsible dipeptides for optimum pH in the G/11 xylanase, for dipeptides can provide position information of the related residues for rational protein design. The responsible dipeptides were found as negative YS and positive SY, GR, MR, and KR. The minimum and maximum optimum pH was calculated as 2.33 and 14.29, respectively. Compared with the known crystal structures of the G/11 xylanase, YS was found mostly in the turn area of beta-strands of S/T surface; and SY was found in the inner part of beta-strands of the S/T area near to the active site of proton donor; and the GR, MR, and KR in the coil region connecting "finger" to the alpha-helix. The result clearly explained the success of shifting of pH 0.5 U to alkaline by the introduction of arginines into S/T area of a xylanase. The result would be useful for xylanase engineering, and the adaptation mechanism to high alkaline was also discussed.

Aspergillus↗

PubMed1998

Microdissection, microchip arrays, and molecular analysis of tumor cells (primary and metastases).

Advances in biotechnology and bioinformatics are offering promise for new breakthroughs in gene discovery and elucidation of gene function. At present, many candidate genes related to cancer pathogenesis have been identified in several types of human cancer, yet frequently their function remains elusive. This is particularly true as it relates to the progression of human cancer. This landscape could change dramatically, however, as technological innovations and improvements continue to revolutionize these fields. High-throughput molecular approaches are emerging, which may become accurate, automated, and cost-effective. For example, DNA arrays on microchips are under development with numerous applications, including the ability to screen genes rapidly for mutations and to study patterns of gene expression on a large scale. Automated systems for microdissection and sequencing are also in their implementation stages. Commensurate with their integration and evolution, these information and technological tools have the potential to offer a more comprehensive understanding of multiple genetic and cellular alterations occurring during cancer initiation, development, and progression. Ultimately, this fundamental knowledge can provide strategies for intervention, prevention, and early diagnosis. This is a US government work. There are no restrictions on its use.

Base Sequence↗

PubMed2003

BIBI, a bioinformatics bacterial identification tool.

BIBI was designed to automate DNA sequence analysis for bacterial identification in the clinical field. BIBI relies on the use of BLAST and CLUSTAL W programs applied to different subsets of sequences extracted from GenBank. These sequences are filtered and stored in a new database, which is adapted to bacterial identification.

Bacteria↗

PubMed2004

Accurate classification of protein structural families using coherent subgraph analysis.

Protein structural annotation and classification is an important problem in bioinformatics. We report on the development of an efficient subgraph mining technique and its application to finding characteristic substructural patterns within protein structural families. In our method, protein structures are represented by graphs where the nodes are residues and the edges connect residues found within certain distance from each other. Application of subgraph mining to proteins is challenging for a number reasons: (1) protein graphs are large and complex, (2) current protein databases are large and continue to grow rapidly, and (3) only a small fraction of the frequent subgraphs among the huge pool of all possible subgraphs could be significant in the context of protein classification. To address these challenges, we have developed an information theoretic model called coherent subgraph mining. From information theory, the entropy of a random variable X measures the information content carried by X and the Mutual Information (MI) between two random variables X and Y measures the correlation between X and Y. We define a subgraph X as coherent if it is strongly correlated with every sufficiently large sub-subgraph Y embedded in it. Based on the MI metric, we have designed a search scheme that only reports coherent subgraphs. To determine the significance of coherent protein subgraphs, we have conducted an experimental study in which all coherent subgraphs were identified in several protein structural families annotated in the SCOP database (Murzin et al, 1995). The Support Vector Machine algorithm was used to classify proteins from different families under the binary classification scheme. We find that this approach identifies spatial motifs unique to individual SCOP families and affords excellent discrimination between families.

Algorithms↗

PubMed2004

Yeast as a touchstone in post-genomic research: strategies for integrative analysis in functional genomics.

The new complexity arising from the genome sequencing projects requires new comprehensive post-genomic strategies: advanced studies in regulatory mechanisms, application of new high-throughput technologies at a genome-wide scale, at the different levels of cellular complexity (genome, transcriptome, proteome and metabolome), efficient analysis of the results, and application of new bioinformatic methods in an integrative or systems biology perspective. This can be accomplished in studies with model organisms under controlled conditions. In this review a perspective of the favourable characteristics of yeast as a touchstone model in post-genomic research is presented. The state-of-the art, latest advances in the field and bottlenecks, new strategies, new regulatory mechanisms, applications (patents) and high-throughput technologies, most of them being developed and validated in yeast, are presented. The optimal characteristics of yeast as a well-defined system for comprehensive studies under controlled conditions makes it a perfect model to be used in integrative, "systems biology" studies to get new insights into the mechanisms of regulation (regulatory networks) responsible of specific phenotypes under particular environmental conditions, to be applied to more complex organisms (e.g. plants, human).

Forecasting↗

PubMed2026

Small serine recombinases are markers for antiphage defense system discovery.

Renewed interest in phage therapy has highlighted a need to understand how bacteria subvert phage infection through antiphage defense systems. Traditionally, strategies to identify antiphage defense systems lack throughput or have limitations for bacterial species where antiphage defense systems are understudied. Herein, we developed a bioinformatic pipeline that uses a small serine recombinase to identify known and unknown antiphage defense systems. Using this approach to query reference genomes and metagenomes, we show that small serine recombinase genes are genetically linked to antiphage defense systems and serve as bait for finding these systems across diverse bacterial phyla. Using co-transcription predictions and statistical analysis of protein domain abundances, we experimentally validated our bioinformatic approach by discovering that KAP P-loop NTPases are fused to putative antiphage domains and reinforce prokaryotic Schlafen proteins as a new class of antiphage defense. Our work shows that small serine recombinases are a reliable genetic marker for the discovery of antiphage defenses across diverse bacterial phyla.

Bacteriophages↗

PubMed2005

RNAi as a bioinformatics consumer.

RNAi has shown great potential for use as a tool for biological discovery, analysis and therapeutics. The involvement of the RNAi pathway in post-transcription silencing, transcriptional silencing and epigenetic silencing as well as its use as a tool for forward genetics and therapeutics throws up several bioinformatics challenges. This paper delineates several areas of research and reviews work that has already been done, the tools that are available and the challenges that lie ahead.

Computational Biology↗

PubMed2003

GénoPlante-Info (GPI): a collection of databases and bioinformatics resources for plant genomics.

Génoplante is a partnership program between public French institutes (INRA, CIRAD, IRD and CNRS) and private companies (Biogemma, Bayer CropScience and Bioplante) that aims at developing genome analysis programs for crop species (corn, wheat, rapeseed, sunflower and pea) and model plants (Arabidopsis and rice). The outputs of these programs form a wealth of information (genomic sequence, transcriptome, proteome, allelic variability, mapping and synteny, and mutation data) and tools (databases, interfaces, analysis software), that are being integrated and made public at the public bioinformatics resource centre of Génoplante: GénoPlante-Info (GPI). This continuous flood of data and tools is regularly updated and will grow continuously during the coming two years. Access to the GPI databases and tools is available at http://genoplante-info.infobiogen.fr/.

Alleles↗

PubMed2005

Using ontologies in PROTEUS for modeling proteomics data mining applications.

Bioinformatics applications are often characterized by a combination of (pre) processing of raw data representing biological elements, (e.g. sequence alignment, structure prediction), and an high level data mining analysis. Developing such applications needs knowledge of both data mining and bioinformatics domains, that can be effectively achieved by combining ontology about the application domain and ontology about the approaches and processes to solve the given problem. In this paper we talk about using ontologies to model proteomics in silico experiments. In particular data mining of mass spectrometry proteomics data is considered.

Computational Biology↗

PubMed2006

ETS-TMPRSS2 fusion gene products in prostate cancer.

Genes playing a role in carcinogenesis have often been identified through analysis of recurrent chromosomal rearrangements. Although such rearrangements are well known in leukemias, lymphomas, and sarcomas, they have not been well characterized in carcinomas. In the October 28, 2005 issue of Science, a study by Tomlins et al. uses bioinformatics techniques to identify candidate oncogenic chromosomal changes based on analysis of outlier gene expression. The authors determined that two ETS transcription factors, ERG and ETV1 were outliers in prostate cancer. The group reports recurrent fusions of the 5' untranslated region of the TMPRSS2 gene to ERG and ETV1 in the majority of prostate cancer samples containing the outlier expression. In cell lines containing the fusion gene, androgen appears to play a role in mediating ETS overexpression. This fusion gene product may play an important role in the development, diagnosis, and treatment of prostate cancer.

Journal Article↗

PubMed2024

Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique.

The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells. Here, we present a protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique called moETM. We describe steps for data preprocessing, multi-omics integration, inclusion of prior pathway knowledge, and cross-omics imputation. As a demonstration, we used the single-cell multi-omics data collected from bone marrow mononuclear cells (GSE194122) as in our original study. For complete details on the use and execution of this protocol, please refer to Zhou et al.1.

Deep Learning↗

PubMed2005

Testing the hypothesis of recent population expansions in nematode parasites of human-associated hosts.

It has been predicted that parasites of human-associated organisms (eg humans, domestic pets, farm animals, agricultural and silvicultural plants) are more likely to show rapid recent population expansions than are parasites of other hosts. Here, we directly test the generality of this demographic prediction for species of parasitic nematodes that currently have mitochondrial sequence data available in the literature or the public-access genetic databases. Of the 23 host/parasite combinations analysed, there are seven human-associated parasite species with expanding populations and three without, and there are three non-human-associated parasite species with expanding populations and 10 without. This statistically significant pattern confirms the prediction. However, it is likely that the situation is more complicated than the simple hypothesis test suggests, and those species that do not fit the predicted general pattern provide interesting insights into other evolutionary processes that influence the historical population genetics of host-parasite relationships. These processes include the effects of postglacial migrations, evolutionary relationships and possibly life-history characteristics. Furthermore, the analysis highlights the limitations of this form of bioinformatic data-mining, in comparison to controlled experimental hypothesis tests.

Animals↗

PubMed2001

The HIB database of annotated UniGene clusters.

SUMMARY: The HumanInfoBase (HIB) is a database of putative human gene transcripts. UniGene clusters are assembled, and the resulting consensus sequences are submitted to the PEDANT software system (Frishman,D., Albermann,K., Hani,J., Heumann,K., Metanomski,A., Zollner,A. and Mewes,H.-W., 2001, Bioinformatics, 17, 44--57) for fully automatic sequence analysis and annotation. Predicted transcripts are classified using a variety of functional and structural categories, and hyperlinks to various databases are provided for additional information. A WWW-based graphical user interface represents the assembly process as well as functionally important sites in the putative transcripts.

Data Collection↗

PubMed2004

e2g: an interactive web-based server for efficiently mapping large EST and cDNA sets to genomic sequences.

e2g is a web-based server which efficiently maps large expressed sequence tag (EST) and cDNA datasets to genomic DNA. It significantly extends the volume of data that can be mapped in reasonable time, and makes this improved efficiency available as a web service. Our server hosts large collections of EST sequences (e.g. 4.1 million mouse ESTs of 1.87 Gb) in precomputed indexed data structures for efficient sequence comparison. The user can upload a genomic DNA sequence of interest and rapidly compare this to the complete collection of ESTs on the server. This delivers a mapping of the ESTs on the genomic DNA. The e2g web interface provides a graphical overview of the mapping. Alignments of the mapped EST regions with parts of the genomic sequence are visualized. Zooming functions allow the user to interactively explore the results. Mapped sequences can be downloaded for further analysis. e2g is available on the Bielefeld University Bioinformatics Server at http://bibiserv.techfak.uni-bielefeld.de/e2g/.

Base Sequence↗

PubMed2005

The bioinformatics resource for oral pathogens.

Complete genomic sequences of several oral pathogens have been deciphered and multiple sources of independently annotated data are available for the same genomes. Different gene identification schemes and functional annotation methods used in these databases present a challenge for cross-referencing and the efficient use of the data. The Bioinformatics Resource for Oral Pathogens (BROP) aims to integrate bioinformatics data from multiple sources for easy comparison, analysis and data-mining through specially designed software interfaces. Currently, databases and tools provided by BROP include: (i) a graphical genome viewer (Genome Viewer) that allows side-by-side visual comparison of independently annotated datasets for the same genome; (ii) a pipeline of automatic data-mining algorithms to keep the genome annotation always up-to-date; (iii) comparative genomic tools such as Genome-wide ORF Alignment (GOAL); and (iv) the Oral Pathogen Microarray Database. BROP can also handle unfinished genomic sequences and provides secure yet flexible control over data access. The concept of providing an integrated source of genomic data, as well as the data-mining model used in BROP can be applied to other organisms. BROP can be publicly accessed at http://www.brop.org.

Bacteria↗